Systemy_dialogowe/evaluate/evaluate.ipynb
2022-04-26 18:33:20 +02:00

271 lines
6.3 KiB
Plaintext
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"id": "443692c0",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "77be6d6c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>hello</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>help</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>request</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>affirm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>request</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>586</th>\n",
" <td>null</td>\n",
" </tr>\n",
" <tr>\n",
" <th>587</th>\n",
" <td>request</td>\n",
" </tr>\n",
" <tr>\n",
" <th>588</th>\n",
" <td>null</td>\n",
" </tr>\n",
" <tr>\n",
" <th>589</th>\n",
" <td>ack</td>\n",
" </tr>\n",
" <tr>\n",
" <th>590</th>\n",
" <td>hello</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>591 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"0 hello\n",
"1 help\n",
"2 request\n",
"3 affirm\n",
"4 request\n",
".. ...\n",
"586 null\n",
"587 request\n",
"588 null\n",
"589 ack\n",
"590 hello\n",
"\n",
"[591 rows x 1 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expected = pd.read_csv('evaluate.tsv', sep='\\t', header=None)\n",
"expected = expected.fillna('null')\n",
"expected"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "b77d4102",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>hello</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>help</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>request</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>affirm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>request</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>586</th>\n",
" <td>null</td>\n",
" </tr>\n",
" <tr>\n",
" <th>587</th>\n",
" <td>request</td>\n",
" </tr>\n",
" <tr>\n",
" <th>588</th>\n",
" <td>null</td>\n",
" </tr>\n",
" <tr>\n",
" <th>589</th>\n",
" <td>ack</td>\n",
" </tr>\n",
" <tr>\n",
" <th>590</th>\n",
" <td>hello</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>591 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"0 hello\n",
"1 help\n",
"2 request\n",
"3 affirm\n",
"4 request\n",
".. ...\n",
"586 null\n",
"587 request\n",
"588 null\n",
"589 ack\n",
"590 hello\n",
"\n",
"[591 rows x 1 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted = pd.read_csv('predicted.tsv', sep='\\t', header=None)\n",
"predicted = predicted.fillna('null')\n",
"predicted"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "79ecc70a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"print(accuracy_score(expected, predicted))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}